FN Archimer Export Format PT J TI Analysing Temporal Variability in Spatial Distributions Using Min–Max Autocorrelation Factors: Sardine Eggs in the Bay of Biscay BT AF Petitgas, Pierre Renard, Didier Desassis, Nicolas Huret, Martin Romagnan, Jean-Baptiste Doray, Mathieu Woillez, Mathieu Rivoirard, Jacques AS 1:1;2:3;3:3;4:2;5:1;6:1;7:2;8:3; FF 1:PDG-RBE-EMH;2:;3:;4:PDG-RBE-STH-LBH;5:PDG-RBE-EMH;6:PDG-RBE-EMH;7:PDG-RBE-STH-LBH;8:; C1 Research Unit EMH, IFREMER, Rue de l’Île d’Yeu, 44300, Nantes, France Research Unit STH, IFREMER, Pointe du Diable, 29280, Plouzané, France Centre de Géosciences, MINES ParisTech, PSL University, 35 Rue Saint Honoré, 77305, Fontainebleau, France C2 IFREMER, FRANCE IFREMER, FRANCE MINES PARISTECH, FRANCE SI NANTES BREST SE PDG-RBE-EMH PDG-RBE-STH-LBH IN WOS Ifremer UPR copubli-france IF 2.576 TC 3 UR https://archimer.ifremer.fr/doc/00599/71157/69915.pdf LA English DT Article CR PELGAS DE ;MAF;Space-time;Habitat;Mapping;Sardine;Bay of Biscay AB This paper presents a novel application of the geostatistical multivariate method known as min–max autocorrelation factors (MAFs) for analysing fisheries survey data in a space–time context. The method was used to map essential fish habitats and evaluate the variability in time of their occupancy. Research surveys at sea on marine fish stocks have been undertaken for several decades now. The data are time series of yearly maps of fish density, making it possible to analyse the space–time variability in fish spatial distributions. Space–time models are key to addressing conservation issues requiring the characterization of variability in habitat maps over time. Here, the variability in fisheries survey data series is decomposed in space and time to address these issues, using MAFs. MAFs were originally developed for noise removal in hyperspectral multivariate data and are obtained using a specific double principal components analysis. Here, MAFs were used to extract the most continuous spatial components that are consistent in time, together with the time series of their amplitudes. MAFs formed an empirical isofactorial model of the data, which served for kriging in each year using all available information across the data series. The approach was applied on the spawning distributions of sardine in the Bay of Biscay from 2000 to 2017. A multivariate approach for dealing with space–time data was adapted here, because the evolution in time was highly variable. Maps were classified using the amplitudes of the MAFs, and groups of typical distributions were identified, which showed different occurrence probabilities in different periods. PY 2020 PD APR SO Mathematical Geosciences SN 1874-8961 PU Springer Science and Business Media LLC VL 52 IS 3 UT 000521969200003 BP 337 EP 354 DI 10.1007/s11004-019-09845-1 ID 71157 ER EF